23 research outputs found
Multicriteria Decision Analysis and Conversational Agents for children with autism
Conversational agents has emerged as a new means of communication and social skills training for children with autism spectrum disorders (ASD), encouraging academia, industry, and therapeutic centres to investigate it further. This paper aims to develop a methodological framework based on Multicriteria Decision Analysis (MCDA) to identify the best , i.e. the most effective, conversational agent for this target group. To our knowledge, it is the first time the MCDA is applied to this specific domain. Our contribution is twofold: i) our method is an extension of traditional MCDA and we exemplify how to apply it to decision making process related to CA for person with autism: a methodological result that would be adopted for a broader range of technologies for person with impairments similar to ASD; ii) our results, based on the above mentioned method, suggest that Embodied Conversational Agent is most appropriate conversational technology to interact with children with ASD
Multiple Appropriate Facial Reaction Generation in Dyadic Interaction Settings: What, Why and How?
According to the Stimulus Organism Response (SOR) theory, all human
behavioral reactions are stimulated by context, where people will process the
received stimulus and produce an appropriate reaction. This implies that in a
specific context for a given input stimulus, a person can react differently
according to their internal state and other contextual factors. Analogously, in
dyadic interactions, humans communicate using verbal and nonverbal cues, where
a broad spectrum of listeners' non-verbal reactions might be appropriate for
responding to a specific speaker behaviour. There already exists a body of work
that investigated the problem of automatically generating an appropriate
reaction for a given input. However, none attempted to automatically generate
multiple appropriate reactions in the context of dyadic interactions and
evaluate the appropriateness of those reactions using objective measures. This
paper starts by defining the facial Multiple Appropriate Reaction Generation
(fMARG) task for the first time in the literature and proposes a new set of
objective evaluation metrics to evaluate the appropriateness of the generated
reactions. The paper subsequently introduces a framework to predict, generate,
and evaluate multiple appropriate facial reactions
Reversible Graph Neural Network-based Reaction Distribution Learning for Multiple Appropriate Facial Reactions Generation
Generating facial reactions in a human-human dyadic interaction is complex
and highly dependent on the context since more than one facial reactions can be
appropriate for the speaker's behaviour. This has challenged existing machine
learning (ML) methods, whose training strategies enforce models to reproduce a
specific (not multiple) facial reaction from each input speaker behaviour. This
paper proposes the first multiple appropriate facial reaction generation
framework that re-formulates the one-to-many mapping facial reaction generation
problem as a one-to-one mapping problem. This means that we approach this
problem by considering the generation of a distribution of the listener's
appropriate facial reactions instead of multiple different appropriate facial
reactions, i.e., 'many' appropriate facial reaction labels are summarised as
'one' distribution label during training. Our model consists of a perceptual
processor, a cognitive processor, and a motor processor. The motor processor is
implemented with a novel Reversible Multi-dimensional Edge Graph Neural Network
(REGNN). This allows us to obtain a distribution of appropriate real facial
reactions during the training process, enabling the cognitive processor to be
trained to predict the appropriate facial reaction distribution. At the
inference stage, the REGNN decodes an appropriate facial reaction by using this
distribution as input. Experimental results demonstrate that our approach
outperforms existing models in generating more appropriate, realistic, and
synchronized facial reactions. The improved performance is largely attributed
to the proposed appropriate facial reaction distribution learning strategy and
the use of a REGNN. The code is available at
https://github.com/TongXu-05/REGNN-Multiple-Appropriate-Facial-Reaction-Generation
A Systematic Review on Reproducibility in Child-Robot Interaction
Research reproducibility - i.e., rerunning analyses on original data to
replicate the results - is paramount for guaranteeing scientific validity.
However, reproducibility is often very challenging, especially in research
fields where multi-disciplinary teams are involved, such as child-robot
interaction (CRI). This paper presents a systematic review of the last three
years (2020-2022) of research in CRI under the lens of reproducibility, by
analysing the field for transparency in reporting. Across a total of 325
studies, we found deficiencies in reporting demographics (e.g. age of
participants), study design and implementation (e.g. length of interactions),
and open data (e.g. maintaining an active code repository). From this analysis,
we distill a set of guidelines and provide a checklist to systematically report
CRI studies to help and guide research to improve reproducibility in CRI and
beyond
Phygital interfaces for people with intellectual disability: an exploratory study at a social care center
AbstractPhygital interaction is a form of tangible interaction where digital and physical contents are combined in such a way that the locus of multimedia information is detached from the physical material(s) manipulated by the user. The use of phygital interaction is supported by several theoretical approaches that emphasize the development of cognitive skills dependent upon embodied interactions with the physical environment. Several studies demonstrate the potential of using phygital technologies for supporting people with intellectual disabilities (ID) in the development of cognitive, sensorimotor, social and behavioral skills. Our research aims at exploring the potential of phygital interaction for (young) adults with ID in a real setting, using a research platform called Reflex as a case study. For this purpose, we ran an empirical study involving 17 participants with ID and 8 specialists, and compared Reflex with approaches making use of only digital contents or paper-based materials. Our findings highlighted the potentials of phygital approaches to perform interventions with people with ID, enhancing their performances with an appreciated interaction method. In addition, the post-study interviews with specialists favoured the adoption of phygital technologies in a social care context
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Affective Robotics For Wellbeing: A Scoping Review
Affective robotics research aims to better understand human social and emotional signals to improve human-robot interaction (HRI), and has been widely used during the last decade in multiple application fields. Past works have demonstrated, indeed, the potential of using affective robots (i.e., that can recognize, or interpret, or process, or simulate human affects) for healthcare applications, especially wellbeing.
This paper systematically review the last decade (January 2013 - May 2022) of HRI literature to identify the main features of affective robotics for wellbeing. Specifically, we focused on the types of wellbeing goals affective robots addressed, their platforms, their shapes, their affective capabilities, and their autonomy in the surveyed studies. Based on this analysis, we list a set of recommendations that emerged, and we also present a research agenda to provide future directions to researchers in the field of affective robotics for wellbeing